4 steps for running a machine learning pilot project

Social Content Writer, IBM Cloud

Running a machine learning pilot project is a great early step on the road to full adoption.

To get started, you’ll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in school.

Here’s how it works:

  1. Identify an opportunity.

Start with a problem tied directly to a specific business outcome. Make sure the subject of your pilot is small enough to tackle and clear enough to measure. For example, if you’re wondering which website user experience (UX) converts most frequently, begin with the smaller challenge of picking images for your product pages. You can return to the larger problem later.

Remember that your pilot project will not only help solve a business problem. It will also help you demonstrate the effectiveness of machine learning to your own stakeholders. This works best if the results are clear cut.

  1. Execute your experiment.

As you run your first data science experiment, remain focused on the specific business problem you chose in the first step.

You can save yourself a lot of trouble by doing everything you can to ensure you only use high-quality, up-to-date sources of data. No matter the end result of your pilot, you’re going to learn a lot. Sometimes, failed pilots are very effective for this reason.

  1. Evaluate your results.

Next, you’ll want to analyze the results of your experiment. What patterns do you see? Is your current business strategy out of touch with the data? If it is, that’s good. It means you’re gaining valuable insights on how to effectively change your approach. More importantly, your pilot is teaching your team how to effectively use data to make decisions.

Look especially hard for any trends in your customers’ behavior, since this will tell you what the future has in store for your business.

  1. Plan your next steps.

You may have found some good answers in your first experiment, but you’ve also probably found a great deal of new questions. Like any good scientist, adjust your next experiment to account for what you’ve learned. Since you already identified a business problem in the first step, it should be clear how to address the next piece of the puzzle.

Your first experiment will help you gain buy-in to expand your machine learning program and add additional sources of data. Machine learning becomes increasingly effective as you expand its reach, since the algorithm tends to produce more insights as it gains additional sources of information.

To learn more about machine learning, download your no-cost copy of Machine Learning for Dummies.

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